title: A new method of concurrently visualizing states, values, and actions in reinforcement based brain machine interfaces.
abstract: This paper presents the first attempt to quantify the individual performance of the subject and of the computer agent on a closed loop Reinforcement Learning Brain Machine Interface (RLBMI). The distinctive feature of the RLBMI architecture is the co-adaptation of two systems (a BMI decoder in agent and a BMI user in environment). In this work, an agent implemented using Q-learning via kernel temporal difference (KTD)(λ) decodes the neural states of a monkey and transforms them into action directions of a robotic arm. We analyze how each participant influences the overall performance both in successful and missed trials by visualizing states, corresponding action value Q, and resulting actions in two-dimensional space. With the proposed methodology, we can observe how the decoder effectively learns a good state to action mapping, and how neural states affect the prediction performance.
authors.forename: Jihye
authors.initials: J
authors.lastname: Bae
authors.forename: Luis G
authors.initials: LG
authors.lastname: Sanchez Giraldo
authors.forename: Eric A
authors.initials: EA
authors.lastname: Pohlmeyer
authors.forename: Justin C
authors.initials: JC
authors.lastname: Sanchez
authors.forename: Jose C
authors.initials: JC
authors.lastname: Principe
mesh_terms: D000465:Algorithms; D000818:Animals; D001522:Behavior, Animal; D062207:Brain-Computer Interfaces; D002144:Callithrix; D007858:Learning; D008839:Microelectrodes; D012054:Reinforcement, Psychology; D012984:Software